Zhen Mei1, Adriana Lori2, Selina M Vattathil3, Patricia A Boyle4, Bekh Bradley5, Peng Jin6, David A Bennett4, Thomas S Wingo7, Aliza P Wingo8. 1. Department of Neurology, Emory University School of Medicine, Atlanta, GA; Xiangya Hospital, Central South University, Changsha, Hunan, China. 2. Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA. 3. Department of Neurology, Emory University School of Medicine, Atlanta, GA. 4. Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL. 5. Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA; Division of Mental Health, Atlanta VA Medical Center, Decatur, GA. 6. Department of Human Genetics, Emory University School of Medicine, Atlanta, GA. 7. Department of Neurology, Emory University School of Medicine, Atlanta, GA; Department of Human Genetics, Emory University School of Medicine, Atlanta, GA. Electronic address: thomas.wingo@emory.edu. 8. Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA; Division of Mental Health, Atlanta VA Medical Center, Decatur, GA. Electronic address: aliza.wingo@emory.edu.
Abstract
OBJECTIVE: A wealth of evidence has linked purpose in life (PiL) to better mental and physical health and healthy aging. Here, the authors aimed to determine important correlates of PiL using a machine learning approach. METHODS: Participants were recruited from retirement communities by the Rush Memory and Aging Project and assessed for childhood experience, adulthood sociodemographic factors (e.g., education, income, marital status), lifestyle and health behavior (e.g., cognitively stimulating activities, exercise, social activities, social network size), psychological factors (e.g., depression, loneliness, perceived discrimination, perceived social support), personality traits (e.g., PiL, harm avoidance), and medical conditions. Elastic Net was implemented to identify important correlates of PiL. RESULTS: A total of 1,839 participants were included in our analysis. Among the 23 variables provided to Elastic Net, 10 were identified as important correlates of PiL. In order of decreasing effect size, factors associated with lower PiL were loneliness, harm avoidance, older age, and depressive symptoms, while those associated with greater PiL were perceived social support, more social activities, more years of education, higher income, intact late-life cognitive performance, and more middle-age cognitive activities. CONCLUSION: Our findings identify potentially important modifiable factors as targets for intervention strategies to enhance PiL. Published by Elsevier Inc.
OBJECTIVE: A wealth of evidence has linked purpose in life (PiL) to better mental and physical health and healthy aging. Here, the authors aimed to determine important correlates of PiL using a machine learning approach. METHODS: Participants were recruited from retirement communities by the Rush Memory and Aging Project and assessed for childhood experience, adulthood sociodemographic factors (e.g., education, income, marital status), lifestyle and health behavior (e.g., cognitively stimulating activities, exercise, social activities, social network size), psychological factors (e.g., depression, loneliness, perceived discrimination, perceived social support), personality traits (e.g., PiL, harm avoidance), and medical conditions. Elastic Net was implemented to identify important correlates of PiL. RESULTS: A total of 1,839 participants were included in our analysis. Among the 23 variables provided to Elastic Net, 10 were identified as important correlates of PiL. In order of decreasing effect size, factors associated with lower PiL were loneliness, harm avoidance, older age, and depressive symptoms, while those associated with greater PiL were perceived social support, more social activities, more years of education, higher income, intact late-life cognitive performance, and more middle-age cognitive activities. CONCLUSION: Our findings identify potentially important modifiable factors as targets for intervention strategies to enhance PiL. Published by Elsevier Inc.
Authors: Tomás Caycho-Rodríguez; Lindsey W Vilca; Mauricio Cervigni; Miguel Gallegos; Pablo Martino; Manuel Calandra; Cesar Armando Rey Anacona; Claudio López-Calle; Rodrigo Moreta-Herrera; Edgardo René Chacón-Andrade; Marlon Elías Lobos-Rivera; Perla Del Carpio; Yazmín Quintero; Erika Robles; Macerlo Panza Lombardo; Olivia Gamarra Recalde; Andrés Buschiazzo Figares; Michael White; Carmen Burgos-Videla Journal: Front Psychol Date: 2022-09-16